CAFE MOCHA: An Integrated Platform for Discovering Clinically Relevant Molecular Changes in Cancer—An Example of Distant Metastasis– and Recurrence-Linked Classifiers in Head and Neck Squamous Cell Carcinoma

Abstract

Purpose With large amounts of multidimensional molecular data on cancers generated and deposited into public repositories such as The Cancer Genome Atlas and International Cancer Genome Consortium, a cancer type agnostic and integrative platform will help to identify signatures with clinical relevance. We devised such a platform and showcase it by identifying a molecular signature for patients with metastatic and recurrent (MR) head and neck squamous cell carcinoma (HNSCC). Methods We devised a statistical framework accompanied by a graphical user interface–driven application, Clinical Association of Functionally Established MOlecular CHAnges ( CAFE MOCHA; https://github.com/binaypanda/CAFEMOCHA), to discover molecular signatures linked to a specific clinical attribute in a cancer type. The platform integrates mutations and indels, gene expression, DNA methylation, and copy number variations to discover a classifier first and then to predict an incoming tumor for the same by pulling defined class variables into a single framework that incorporates a coordinate geometry–based algorithm called complete specificity margin-based clustering, which ensures maximum specificity. CAFE MOCHA classifies an incoming tumor sample using either its matched normal or a built-in database of normal tissues. The application is packed and deployed using the install4j multiplatform installer. We tested CAFE MOCHA in HNSCC tumors (n = 513) followed by validation in tumors from an independent cohort (n = 18) for discovering a signature linked to distant MR. Results CAFE MOCHA identified an integrated signature, MR44, associated with distant MR HNSCC, with 80% sensitivity and 100% specificity in the discovery stage and 100% sensitivity and 100% specificity in the validation stage. Conclusion CAFE MOCHA is a cancer type and clinical attribute agnostic statistical framework to discover integrated molecular signatures.